A lithographic tool uses many components such as, for example, reticles and optical subsystems to ensure precise image transfer onto a wafer to produce a desired microelectronic device. But, the ability to produce high quality microelectronic devices and reduce yield losses is dependent upon maintaining the surfaces of critical components substantially defect-free. This would include, for example, maintaining the surfaces of the optical elements free of defects and debris which can impair the overall performance of the lithographic tool, e.g., impair illumination power of the lithography tool. This is of particular concern as finer features are required on the microelectronic device.
By way of example, a photolithography apparatus (exposure apparatus) includes an illumination system which is designed to project radiant energy (e.g., light) through a mask pattern on a reticle. By way of some examples, the illumination source can be g-line (436 nm), i-line (365 nm), KrF excimer laser (248 nm), ArF excimer laser (193 nm) or F2 laser (157 nm). The projection optics (optical elements) can include, depending on the particular illumination source, glass materials (quartz and fluorite), catadioptric or refractive systems, electron lenses and deflectors.
As the illumination source projects through the optical elements, contamination may build-up on the surface of the optical elements (e.g., glass, etc.). It is theorized that the contamination build-up is due to the interaction between the optical elements and wavelength from the illumination source. One type of contamination may be bulk glass damage (e.g., decomposition) which results from very high UV exposure doses. Other types of contamination may also arise from airborne molecular contaminants that are not completely filtered from the system. Sources of airborne molecular contaminants include outgassing from resist processes or materials of construction, ambient cleanroom contamination, or from purge gas streams.
In any event, the overall power degradation could be viewed as a function of multiple contributors including, for example, thin film contamination, bulk glass damage, as well as other known or unknown contributors. This contamination absorbs illumination energy which may impede the illumination power of the illumination source at the wafer surface.
In view of the fact that the impedance of illumination power will begin to impair the performance of the lithographic tool, it is important to have stringent control of contaminant levels of the equipment environment to maximize the lifetime of the optical elements. Without such controls, there is an increase in the risk for contamination which can lead to increased equipment downtime for troubleshooting and reactive maintenance.
The monitoring of the equipment is not only a physical inspection of the systems and subsystems, but also includes the use of predictive tools, for example, to determine and predict maintenance events. The physical inspection includes sensors near the wafer to determine illumination power at the wafer surface. The predictive tools, on the other hand, use the data from the sensors to predict illumination power performance in an attempt to optimize the associated system maintenance activities related to the cleaning or replacement of the optical elements of the optical subsystems.
Currently, tools used in predicting illumination power degradation are based on linear trend models for the purpose of predicting scheduled preventive maintenance activities. In operation, such tools plot the trend data, e.g., power vs. time, for a period of time. The data is then forced into a linear fit regardless of actual signature shape in the data. This leads to inaccurate predictions and false conclusions.
As should be understood, a linear growth rate results in an exponential decay and is termed “normal degradation”. However, the combined growth rate is linear only if each contributor is linear, independently. But, the combined growth rate is non-linear if just one contributor is non-linear independently. The result of a non-linear growth rate is termed “accelerating degradation” or “abnormal degradation”.
By using the current linear method of predictive modeling, sudden, catastrophic failures cannot be adequately predicted. In such cases, the sudden, catastrophic failure, no matter how few, are unscheduled and can severely impact chip production, i.e., significantly impairs chip yield. Also, such known predictive modeling techniques may lead to premature or inaccurate performance threshold reach dates resulting in premature maintenance based on a linear model. This too can significantly impair chip yield. Additionally, known predictive modeling cannot distinguish between normal and abnormal degradation and may miss indicators of potential tool issues such as low rate with high acceleration.
Accordingly, there exists a need in the art to overcome the deficiencies and limitations described hereinabove.